1. Technical Field
This invention pertains in general to Internet advertising and in particular to methods of incrementally training models for advertisement targeting using streaming data.
2. Description of Related Art
In a real-time bidding environment of an Internet advertisement exchange, a potential advertiser who may want to bid on an advertisement opportunity has a very short period of time to determine whether the characteristics of the advertisement opportunity make the advertisement opportunity valuable enough to submit a bid. Characteristics of the advertisement opportunity include the URL where the ad will appear and the dimensions of the ad slot. In addition, some of the most important characteristics of the advertisement opportunity include the features that are known and/or can be inferred about the user who would view an advertisement that fills the advertisement opportunity. Such features can be determined based at least in part on a consumption history associated with the user.
In general, advertising campaign managers are seeking to maximize the number of conversions for an advertising campaign. A conversion occurs when a user takes an action deemed desirable by the advertiser, such as buying an advertised product, visiting a website, signing up for a service, etc. The precise actions that define a conversion can be set by the advertising campaign manager. By analyzing features from the consumption histories of converters versus non-converters, models can be developed to predict whether a user is likely to become a converter. Such models can be applied in the real-time bidding environment of an Internet advertising exchange to predict whether the user who would view an advertisement that fills the advertisement opportunity is likely to convert. In other words, by applying a model to a consumption history, the advertiser can estimate the likelihood that the user who would view the advertisement would become a converter, and the more likely it is that the user would convert, the more valuable the advertising opportunity is to the advertiser. Thus, an advertiser can determine whether the advertisement opportunity is valuable enough to the advertising campaign to submit a bid and to determine an amount of the bid to submit.
Although the benefits described above of using models to predict whether a user is likely to become a converter are well-recognized in the field, there are many challenges to the successful implementation and use of models. For example, models are most useful when they can capitalize on quickly developing and subsiding trends in user behavior, but for convenience, most models are built from stale data that by definition are not responsive to such trends. In addition, most models are built from vast repositories of data that require significant re-processing whenever an update to the model is required, thus further delaying the implementation of the updated model.